
\documentclass{beamer}
\usetheme{Berlin}
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\newenvironment{proenv}{\only{\setbeamercolor{local structure}{fg=green}}}{}
\newenvironment{midenv}{\only{\setbeamercolor{local structure}{fg=yellow}}}{}
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\usepackage{subfigure}
\usepackage{ulem}

\title{Variable Mapping for Transfer Learning}
\author{Benjamin Bittner, Koen Bonenkamp, Dario Chiappetta}
\date{February 3, 2012}

\begin{document}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% TITLE/TABLE OF CONTENTS

\begin{frame}
\titlepage
\begin{center}
{\small Maarten van Someren (supervisor)}
\end{center}
\end{frame}

\begin{frame}{Table of Contents}
\tableofcontents
\end{frame}

%%%%%%%%%%%%%%%%%%%%%%%% KOEN

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% INTRODUCTION

\section{Introduction}

\subsection{General introduction}
\begin{frame}{Terminology}
\begin{itemize}
\item Transfer Learning
\item Example
\end{itemize}
\end{frame}

\begin{frame}{Two examples}
\begin{itemize}
\item 'Trivial' example
\item Nontrivial example
\end{itemize}
\end{frame}

%%%%%%%%%%%%%%%%%%%%%%%% BENJAMIN
\subsection{Wireless sensor networks / monitoring human activites}

\begin{frame}{Assumption}
\begin{center}
{\bf $\rightarrow$ Living patterns of people are {\it similar}}
\end{center}
\end{frame}


%%%%%%%%%%%%%%%%%%%%%%%% DARIO

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% SENSOR-SENSOR MAPPING

\section{Sensor-to-sensor mapping}

\subsection{Statistical profile}

\begin{frame}{Statistical profile}
\begin{columns}[ll]

  \column{.5\textwidth}
    \begin{itemize}
    \item EMPTY
    \end{itemize}

  \column{.6\textwidth}
    \begin{itemize}
    \item EMPTY
    \end{itemize}

\end{columns}
\end{frame}

\subsection{Relational profile}

\begin{frame}{Relational profile}
\begin{center}
{\bf Empirical evidence in favour}
\end{center}
\end{frame}

%%%%%%%%%%%%%%%%%%%%%%%% KOEN

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% METAFEATURE-METAFEATURE MAPPING

\section{Metafeature-metafeature mapping}
\subsection{Activation time}

\begin{frame}
\end{frame}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% AVERAGE DAY

\section{Modeling an 'average' day}
\begin{frame}{Average day}
\end{frame}

%%%%%%%%%%%%%%%%%%%%%%%% BENJAMIN





%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% GROUP-TO-GROUP MATCHING

\section{Group-to-group matching}

\begin{frame}{How to compare (groups of) sensors?}

\begin{center}
\begin{figure}
\includegraphics[width=0.7\textwidth]{heuristic.jpg}
\end{figure}
\end{center}

Blue circle: expected mapping.

\end{frame}

\begin{frame}{Experiments}

Match groups of sensors. Each sensor has own model (Gaussian).

\begin{columns}[t]

\column{.5\textwidth}

\begin{itemize}
\itemsep0em
\small
\item<pro@1-> BathroomDoor/B $\rightarrow$ Toilet/A
\item<con@1-> SleepDoor/B $\rightarrow$ KitchHeat/A
\item<pro@1-> Toilet/B $\rightarrow$ BathroomDoor/A
\item<pro@1-> Outside/B $\rightarrow$ Outside/A
\item<pro@1-> Sleep/B $\rightarrow$ SleepDoor/A
\item<pro@1-> Kitchen/B $\rightarrow$ Kitchen/A
\item<pro@1-> KitchStor/B $\rightarrow$ KitchStor/A
\item<pro@1-> KitchHeat/B $\rightarrow$ KitchHeat/A
\item<mid@1-> Bathroom/B $\rightarrow$ SleepDoor/A
\end{itemize}

\column{.5\textwidth}

\begin{itemize}
\itemsep0em
\small
\item<con@1-> KitchHeat/A $\rightarrow$ SleepDoor/B
\item<pro@1-> Toilet/A $\rightarrow$ BathroomDoor/B
\item<pro@1-> BathroomDoor/A $\rightarrow$ Toilet/B
\item<con@1-> Outside/A $\rightarrow$ Kitchen/B
\item<pro@1-> SleepDoor/A $\rightarrow$ Sleep/B
\item<con@1-> Kitchen/A $\rightarrow$ Outside/B
\item<con@1-> KitchStor/A $\rightarrow$ Bathroom/B
\end{itemize}

\end{columns}

\end{frame}

%Different house layout and lifestyle.
%\begin{itemize}
%\itemsep0em
%\item<pro@1-> Bathroom/C $\rightarrow$ Toilet/B
%\item<con@1-> Other/C $\rightarrow$ Kitchen/B
%\item<con@1-> KitchStor/C $\rightarrow$ BathroomDoor/B
%\item<con@1-> Kitchen/C $\rightarrow$ Bathroom/B
%\item<pro@1-> SleepDoor/C $\rightarrow$ Sleep/B
%\item<pro@1-> KitchHeat/C $\rightarrow$ KitchHeat/B
%\item<con@1-> Outside/C $\rightarrow$ KitchStor/B
%\item<con@1-> Toilet/C $\rightarrow$ SleepDoor/B
%\item<con@1-> Sleep/C $\rightarrow$ Outside/B
%\item<con@1-> BathroomDoor/C $\rightarrow$ KitchHeat/B
%\end{itemize}
%{\bf BUT,} would transfer learning be meaningful at all in such a case?
%\end{frame}


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% CONCLUSION

\section{Conclusion}

\begin{frame}
\begin{center}
\begin{figure}
\includegraphics[width=0.7\textwidth]{conclusions.jpg}
\end{figure}
\end{center}
\end{frame}

\begin{frame}{Importance of Transfer Learning}
\begin{itemize}
\item transfer learning is important
\item central aspect of human intelligence
\item automatically aligning two related problems
% \item important precondition for transfer learning
% \item related work we considered did it manually
\end{itemize}
\end{frame}

\begin{frame}{Findings}
\begin{itemize}
\item clustering based on temporal information is possible
\item automated matching of problem structures is possible
\item quality of training data determines real-world performance
\end{itemize}
\end{frame}

\begin{frame}{Future Work}
\begin{itemize}
\item lots of! (would even make a nice PhD research project)
\item e.g.: does transfer performance improve by using automated matching?
\item better grasp of relationships between features (sensors)
\item other ways of modelling sensors / groups of sensors
\item comparison of groups of features (meta-features)
\item $\dots$
\end{itemize}
\end{frame}

\begin{frame}
\begin{center}
\begin{figure}
\includegraphics[width=0.7\textwidth]{thank_you.jpg}
\end{figure}
\end{center}
\end{frame}

\appendix

\begin{frame}{How to compare groups of sensors?}

\vspace{-10pt}
\begin{figure}[t]
  \small
  \begin{tabbing}[c]
  fo \= fo \= fo \= fo \= \kill
  \textbf{function} clusterDivergence (clusterSmall, clusterBig)\\
    1\> avgMinDivergence := 0 \\
    2\> for {\bf $s_S$} in clusterSmall: \\
    3\> \> minDiv := inf \\
    4\> \> for {\bf $s_B$} in clusterBig: \\
    5\> \> \> curMinDiv := KL($s_S,s_B$) \\
    6\> \> \> {\bf if} curMinDiv $<$ minDiv: \\
    7\> \> \> \> minDiv := curMinDiv \\
    8\> \> avgMinDivergence := avgMinDivergence + minDiv \\
    9\> avgMinDivergence := avgMinDivergence / length(clusterSmall) \\
   10\> {\bf return} avgMinDivergence
  \end{tabbing}
\end{figure}

Use this distance measure to identify similar clusters in source and target
domain.

\end{frame}

\begin{frame}{How to match (groups of) sensors?}

\begin{center}
\begin{figure}
\includegraphics[width=0.7\textwidth]{heuristic.jpg}
\end{figure}
\end{center}

Blue circle: expected mapping.

\end{frame}


\end{document}

